Today’s Topics

  1. Customizing visualizations
  2. Making static visualizations interactive with plotly
  3. Creating interactive maps with leaflet

Setup

Loading in our Libraries

Read in 2023 Sustainable Development Data with read_csv() and here()

sdr_data <- read_csv(here("data/SDR-2023-Data.csv"))

Clean column names

sdr_data <- sdr_data %>% 
  clean_names()

Customize a Histogram

Let’s start with the histogram you created last lesson with ggplot() and geom_histogram()

ggplot(sdr_data, aes(x = goal_4_score, fill=regions_used_for_the_sdr)) +
  geom_histogram()

Looks Nice, but let’s improve it!

  • add a theme with theme_minimal()
  • add a color palette with scale_fill_viridis_d()
  • add a title, x axis label, y axis label, and legend label with labs()

We start with the same exact code and add to it with +

ggplot(sdr_data, aes(x = goal_4_score, fill=regions_used_for_the_sdr)) +
  geom_histogram() +
  theme_minimal() +
  scale_fill_viridis_d() +
  labs(title = "Distributions of SDG 4 Scores",
       x = "SDG 4 Score",
       y = "Number of Countries",
       fill = "Region")

Awesome, looking much better!

  • However, it’s still a little tricky to tell exactly how many countries in each region have each SDG 4 Score
  • There’s a few ways to go address this, one of them is to make the plot interactive with ggplotly()

Making static visualizations interactive

Interactive visualizations are a really exciting part of data science! Interactivity engages the viewer by allowing them to explore the data in a way they cannot with static visualizations

  • The great part is that with the ggplotly() function from the plotly package, making interactive visualizations is very simple

    • First we create the plot with the exact same code

      • The only difference is that we assign the plot a name with <- the same way we do with dataframes or lists

      • Next we put the name that we give the plot into the ggplotly function

goal_4_histogram <- ggplot(sdr_data, aes(x = goal_4_score, fill=regions_used_for_the_sdr)) +
  geom_histogram() +
  theme_minimal() +
  scale_fill_viridis_d() +
  labs(title = "Distributions of SDG 4 Scores",
       x = "SDG 4 Score",
       y = "Number of Countries",
       fill = "Region")

ggplotly(goal_4_histogram)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 27 rows containing non-finite values (`stat_bin()`).

Epic! Now we can hover over the histogram and get some info

  • For example: We can see 2 countries in the LAC region have a score of 68

We can also double-click the boxes in the legend to only view the regions we’re interested in

Creating interactive maps with leaflet

This takes a few fun steps to do

  1. We need data on countries and their physical location on the globe

  • Lucky for us the rnaturalearth package has this information for us

    • We use the ne_countries() function to access the data and we assign it the name world
world <- ne_countries(scale = "medium", returnclass = "sf")
- Now we have a dataframe named *world* in our environment
- It has 241 locations/countries (rows) and 64 columns describing each location/country
- We are only interested in 3 columns:
world <- world %>% 
  select(name_long, iso_a3, geometry)
# Rename a column in a data frame or matrix
colnames(sdr_data)[which(colnames(sdr_data) == "country_code_iso3")] <- "iso_a3"
joined_df <- left_join(sdr_data, world, by = "iso_a3")
world_df_joined <- st_as_sf(joined_df)
world_df_joined <- st_transform(world_df_joined, "+proj=longlat +datum=WGS84")
mytext <- paste(
    "Country: ", world_df_joined$country,"<br/>", 
    "Goal 7 Score: ", round(world_df_joined$goal_7_score, 2), 
    sep="") %>%
  lapply(htmltools::HTML)

leaflet(world_df_joined) %>% 
  addTiles()  %>% 
  setView( lat=10, lng=0 , zoom=2) %>%
  addPolygons(stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5, color = ~colorQuantile("YlOrRd", goal_7_score)(goal_7_score), label = mytext)